Towards Data-Driven On-Demand Transport

Malcolm Egan (1, 2) , Jan Drchal (3) , Jan Mrkos (3) , Michal Jakob (3)

1 SOCRATE - Software and Cognitive radio for telecommunications
2 MARACAS - Modèle et algorithmes pour des systèmes de communication fiables
3 FEL CTU - Faculty of Electrical Engineering [Prague]

On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeoffs between passenger prices, individual driver profits and provider revenue. However, pricing and allocations—known as mechanisms—are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in on-demand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into efficient pricing and allocation in on-demand transport.

Mots clés: auctions; taxis; on-demand transport; market mechanisms

Domaines: Apprentissage [cs.LG]; Système multi-agents [cs.MA]; Infrastructures de transport

Malcolm Egan, Jan Drchal, Jan Mrkos, Michal Jakob. Towards Data-Driven On-Demand Transport. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2018, 5 (14), pp.1-10. ⟨10.4108/eai.27-6-2018.154835⟩. ⟨hal-01839452⟩